However, understanding what really happens inside a CapsNet is still an open challenge. Indeed, for a given input image, there are too many active (and each-other coupled) capsules, making the routing algorithm connections still difficult to understand, as the coupling coefficients typically have each-other similar values (Fig. REF ), non exploiting the routing algorithm potential and making architectures without routing performing comparably [1]}. Furthermore, backward and forward passes of a CapsNet come at an enormous computational cost, since the number of trainable parameters is very high. For example, the CapsNet model deployed on the MNIST dataset by Sabour et al.[2]} is composed by an encoder and a decoder part. The full architecture has \(8.2\) M of parameters. Do we really need that amount of trainable parameters to achieve competitive results on such a task? Recently, many pruning methods were applied to CNNs in order to reduce the complexity of the networks, enforcing sparse topologies [3]}, [4]}, [5]}: is it possible to tailor one of these approaches with not only the purpose of lowering the parameters, but aiding the model's interpretability?
